Statistical Analysis of Noise in MRI
Modeling, Filtering and Estimation
Nonfiction, Health & Well Being, Medical, Reference, Biostatistics, Computers, Application Software, General Computing
| Author: | Santiago Aja-Fernández, Gonzalo Vegas-Sánchez-Ferrero | ISBN: | 9783319399348 |
| Publisher: | Springer International Publishing | Publication: | July 12, 2016 |
| Imprint: | Springer | Language: | English |
| Author: | Santiago Aja-Fernández, Gonzalo Vegas-Sánchez-Ferrero |
| ISBN: | 9783319399348 |
| Publisher: | Springer International Publishing |
| Publication: | July 12, 2016 |
| Imprint: | Springer |
| Language: | English |
This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques. Features: provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques; describes noise and signal estimation for MRI from a statistical signal processing perspective; surveys the different methods to remove noise in MRI acquisitions from a practical point of view; reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions; examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal; includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets.
This unique text presents a comprehensive review of methods for modeling signal and noise in magnetic resonance imaging (MRI), providing a systematic study, classifying and comparing the numerous and varied estimation and filtering techniques. Features: provides a complete framework for the modeling and analysis of noise in MRI, considering different modalities and acquisition techniques; describes noise and signal estimation for MRI from a statistical signal processing perspective; surveys the different methods to remove noise in MRI acquisitions from a practical point of view; reviews different techniques for estimating noise from MRI data in single- and multiple-coil systems for fully sampled acquisitions; examines the issue of noise estimation when accelerated acquisitions are considered, and parallel imaging methods are used to reconstruct the signal; includes appendices covering probability density functions, combinations of random variables used to derive estimators, and useful MRI datasets.
